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Laser powder bed fusion is one of the most promising additive manufacturing techniques for printing complex-shaped metal components. However, the formation of subsurface porosity poses a significant risk to the service lifetime of the printed parts. In-situ monitoring offers the possibility to detect porosity already during manufacturing. Thereby, process feedback control or a manual process interruption to cut financial losses is enabled.
Short-wave infrared thermography can monitor the thermal history of manufactured parts which is closely connected to the probability of porosity formation. Artificial intelligence methods are increasingly used for porosity prediction from the obtained large amounts of complex monitoring data. In this study, we aim to identify the potential and the challenges of deep-learning-assisted porosity prediction based on thermographic in-situ monitoring.
Therefore, the porosity prediction task is studied in detail using an exemplary dataset from the manufacturing of two Haynes282 cuboid components. Our trained 1D convolutional neural network model shows high performance (R² score of 0.90) for the prediction of local porosity in discrete sub-volumes with dimensions of (700 x 700 x 40) μm³.
It could be demonstrated that the regressor correctly predicts layer-wise porosity changes but presumably has limited capability to predict differences in local porosity. Furthermore, there is a need to study the significance of the used thermogram feature inputs to streamline the model and to adjust the monitoring hardware. Moreover, we identified multiple sources of data uncertainty resulting from the in-situ monitoring setup, the registration with the ground truth X-ray-computed tomography data and the used pre-processing workflow that might influence the model’s performance detrimentally.
The detection of internal irregularities is crucial for quality assessment in metal-based additive manufacturing (AM) technologies such as laser powder bed fusion (L-PBF). The utilization of in-process thermography as an in situ monitoring tool in combination with post-process X-ray micro computed tomography (XCT) as a reference technique has shown great potential for this aim. Due to the small irregularity dimensions, a precise registration of the datasets is necessary as a
requirement for correlation. In this study, the registration of thermography and XCT reference datasets of a cylindric specimen containing keyhole pores is carried out for the development of a porosity prediction model. The considered datasets show variations in shape, data type and dimensionality, especially due to shrinkage and material elevation effects present in the manufactured part. Since the resulting deformations are challenging for registration, a novel preprocessing methodology is introduced that involves an adaptive volume adjustment algorithm which is based on the porosity distribution in the specimen. Thus, the implementation of a simple three-dimensional image-to-image registration is enabled. The results demonstrate the influence of the part deformation on the resulting porosity location and the importance of registration in terms of irregularity prediction.
The prediction of porosity is a crucial task for metal based additive manufacturing techniques such as laser powder bed fusion. Short wave infrared thermography as an in-situ monitoring tool enables the measurement of the surface radiosity during the laser exposure. Based on the thermogram data, the thermal history of the component can be reconstructed which is closely related to the resulting mechanical properties and to the formation of porosity in the part. In this study, we present a novel framework for the local prediction of porosity based on extracted features from thermogram data. The framework consists of a data pre-processing workflow and a supervised deep learning classifier architecture. The data pre-processing
workflow generates samples from thermogram feature data by including feature information from multiple subsequent layers.
Thereby, the prediction of the occurrence of complex process phenomena such as keyhole pores is enabled. A custom convolutional neural network model is used for classification. Themodel is trained and tested on a dataset from thermographic in-situ monitoring of the manufacturing of an AISI 316L stainless steel test component. The impact of the pre-processing parameters and the local void distribution on the classification performance is studied in detail. The presented model achieves an accuracy of 0.96 and an f1-Score of 0.86 for predicting keyhole porosity in small sub-volumes with a dimension of (700 × 700 × 50) μm3. Furthermore, we show that pre-processing parameters such as the porosity threshold for sample
labeling and the number of included subsequent layers are influential for the model performance. Moreover, the model prediction is shown to be sensitive to local porosity changes although it is trained on binary labeled data that disregards the actual sample porosity.
Planar tomography and numerical analysis for damage characterization of impact loaded RC plates
(2020)
The damage analysis of reinforced concrete (RC) is of high interest for reasons of effective maintenance and structural safe-ty of buildings. The damage structures of RC plates loaded by an impact were investigated, applying X-ray planar tomogra-phy and finite element method (FEM). Planar tomography allows getting three-dimensional information of the RC elements and the damage including crack, spalling and scabbing. The FEM model validated on the tomography data justifies the appli-cation for further predictions of the damage description. In this study, we investigated concrete plates of three different thick-ness subjected to impacts at different low- and medium-velocity, whereby the used impactor had a flat tip, which resulted in small penetrations on the front side and scabbing on the rear side. In order to quantify the damage, the damage volume and its distribution through the plate were computed and the correlations between degree of damage and impact velocity were found out.
The one-dimensional thermal quadrupole method is used to evaluate a pulsed thermography measurement at delaminations in a glass-fiber reinforced plastic plate quantitatively. The large-scale delaminations have been induced by tension overload and are air-filled and are usually located at the same depth as the notch bottom of a notch on the rear side. While classical evaluation methods like pulsed phase thermography and thermal Signal reconstruction are focused on the delamination depth only, the thermal quadrupole method determines spatially resolved two parameters for delaminations, delamination depth and local thermal resistance. Interestingly, lateral heat flows do not disturb this kind of depth evaluation.
Active thermography is a well suited non-destructive testing method for the challenging inspection of wind rotor blades. Since the GFRP structures are up to some centimetres thick, long pulse heating is required to provide an appropriate energy input into the structure. So far, no best practice exists to guarantee a reliable detection of deep-lying flaws. In this work, a step wedge specimen having a maximum thickness of 34mm is systematically investigated by experiment and well-matched simulations to assess the influence of the experimental parameters, like the absorbed energy, on thermal contrasts. Finally, a scheme to conduct full-scale test of a wind rotor blade in less than three hours is proposed.
NMR is gaining increasing interest in civil engineering applications for the use of microstructure characterization as e.g. pore size determination and monitoring of moisture transport in porous materials. In this study, the use of NMR as a tool for pore size characterization was investigated. For our study we used screed and synthetic materials at partial and full saturation. A successful determination could be achieved when having a reference or calibration method, although partly diffusion effects have been registered. Due to these diffusion effects, for the determination of pore size distributions of synthetic materials another NMR device was needed. Finally, the determination of the surface relaxivity of screed (50 μm/s) led to a higher value than first expected from literature.
Since nuclear magnetic resonance with focus on 1H protons is highly sensitive to pore filling fluids, it is nowadays often applied for the investigation of porous media. Mainly in materials research and especially in the field of non-destructive testing in civil engineering it is increasingly used. Scientific questions about and based on NMR meanwhile cover a broad spectrum. To give an overview, we have reviewed various studies dealing with the determination of moisture contents and parameters such as the pore-size distribution, surface relaxivity, porosity, etc. In some papers, the monitoring of moisture transport in connection with degradation processes or admixtures was the main objective. In other papers, NMR was used for pore space analysis or even applied on site to assess the state of conservation of cultural heritage. Building materials that have been investigated in the presented studies are for example cement, concrete, woods, sandstones etc. In this paper, short descriptions and the significant results of the reviewed articles are summarized and their measurement problems and discrepancies are pointed out. A special feature of this review article is the concise tabular compilation of determined 𝑇1 and 𝑇2 relaxation times, as well as of surface relaxivity values for various materials and components. Finally, relevant aspects are summed up and conclusions about the increasing potential of NMR relaxometry for investigations of porous building materials are drawn, followed by an outlook about future applications and the need for technical development.
Thanks to its good strength/mass ratio, a glass fibre reinforced plastic (GFRP) composite is a common material widely used in aviation, power production, automotive and other industries. In its turn, active infrared (IR) nondestructive testing (NDT) is a common inspection technique for detecting and characterizing structural defects in GFRP. Materials to be tested are typically subjected to optical heating which is supposed to occur on the material surface. However, GFRP composite is semitransparent for optical radiation of both visual and IR spectral bands. Correspondingly, the inspection process represents a certain combination of both optical and thermal phenomena. Therefore, the known characterization algorithms based on pure heat diffusion cannot be applied to semi-transparent materials. In this study, the phenomenon of GFRP semi-transparency has been investigated numerically and experimentally in application to thermal NDT. Both Xenon flash tubes and a laser have been used for thermal stimulation of opaque and semi-transparent test objects. It has been shown that the Penetration of optical heating radiation into composite reduces detectability of shallower defects, and the signal-to-noise ratio can be enhanced by applying the technique of thermographic signal reconstruction (TSR). In the inspection of the semi-transparent GFRP composite, the most efficient has been the laser heating followed by the TSR data processing. The perspectives of defect characterization of semi-transparent materials by using laser heating are discussed. A neural network has been used as a candidate tool for evaluating defect depth in composite materials, but its training should be performed in identical with testing conditions.
An Ontology-Based Approach to Enable Data-Driven Research in the Field of NDT in Civil Engineering
(2021)
Although measurement data from the civil engineering sector are an important basis for scientific analyses in the field of non-destructive testing (NDT), there is still no uniform representation of these data. An analysis of data sets across different test objects or test types is therefore associated with a high manual effort. Ontologies and the semantic web are technologies already used in numerous intelligent systems such as material cyberinfrastructures or research databases. This contribution demonstrates the application of these technologies to the case of the 1H nuclear magnetic resonance relaxometry, which is commonly used to characterize water content and porosity distri-bution in solids. The methodology implemented for this purpose was developed specifically to be applied to materials science (MS) tests. The aim of this paper is to analyze such a methodology from the perspective of data interoperability using ontologies. Three benefits are expected from this ap-proach to the study of the implementation of interoperability in the NDT domain: First, expanding knowledge of how the intrinsic characteristics of the NDT domain determine the application of semantic technologies. Second, to determine which aspects of such an implementation can be improved and in what ways. Finally, the baselines of future research in the field of data integration for NDT are drawn.
Undetected and undesired microstructural variations in components produced by laser powder bed fusion are a major challenge, especially for safety-critical components. In this study, an in-depth analysis of the microstructural features of 316L specimens produced by laser powder bed fusion at different levels of volumetric energy density and different levels of inter layer time is reported. The study has been conducted on specimens with an application relevant build height
(>100 mm). Furthermore, the evolution of the intrinsic preheating temperature during the build-up of specimens was monitored using a thermographic in-situ monitoring set-up. By applying recently determined emissivity values of 316L powder layers, real temperatures could be quantified. Heat accumulation led to preheating temperatures of up to about 600 °C. Significant differences in the preheating temperatures were discussed with respect to the individual process parameter combinations, including the build height. A strong effect of the inter layer time on the heat accumulation was observed. A shorter inter layer time resulted in an increase of the preheating temperature by more than a factor of 2 in the upper part of the specimens compared to longer inter layer times. This, in turn, resulted in heterogeneity of the microstructure and differences in material properties within individual specimens. The resulting differences in the microstructure were analyzed using electron back scatter diffraction and scanning electron microscopy. Results from chemical analysis as well as electron back scatter diffraction measurements indicated stable conditions in terms of chemical alloy composition and austenite phase content for the used set of parameter combinations. However, an increase of the average grain size by more than a factor of 2.5 could be revealed within individual specimens. Additionally, differences in feature size of the solidification cellular substructure were examined and a trend of increasing cell sizes was observed. This trend was attributed to differences in solidification rate and thermal gradients induced by differences in scanning velocity and preheating temperature. A change of the thermal history due to intrinsic preheating could be identified as the main cause of this heterogeneity. It was induced by critical combinations of the energy input and differences in heat transfer conditions by variations of the inter layer time. The microstructural variations were directly correlated to differences in hardness.
Material qualification for laser powder bed fusion (L-PBF) processes are often based on results derived from additively manufactured (AM) bulk material or small density cubes, although it is well known that the part geometry has a tremendous influence on the heat flux and, therefore, on the thermal history of an AM component. This study shows experimentally the effect of simple geometrical obstructions to the heat flux on cooling behavior and solidification conditions of 316L stainless steel processed by L-PBF. Additionally, it respects two distinct inter layer times (ILT) as well as the build height of the parts. The cooling behavior of the parts is in-situ traced by infrared (IR) thermography during the built-up. The IR signals reveal significant differences in cooling conditions, which are correlated to differences in melt pool geometries. The acquired data and results can be used for validation of computational models and improvements of quality assurance.
Recording the temperature distribution of the layer under construction during laser powder bed fusion (L-PBF) is of utmost interest for a deep process understanding as well as for quality assurance and in situ monitoring means. While having a notable number of thermal monitoring approaches in additive manufacturing (AM), attempts at temperature calibration and emissivity determination are relatively rare. This study aims for the experimental temperature adjustment of an off-axis infrared (IR) thermography setup used for in situ thermal data acquisition in L-PBF processes. The temperature adjustment was conducted by means of the so-called contact method using thermocouples at two different surface conditions and two different materials: AISI 316L L-PBF bulk surface, AISI 316L powder surface, and IN718 powder surface. The apparent emissivity values for the particular setup were determined. For the first time, also corrected, closer to real emissivity values of the bulk or powder surface condition are published. In the temperature region from approximately 150 °C to 580 °C, the corrected emissivity was determined in a range from 0.2 to 0.25 for a 316L L-PBF bulk surface, in a range from 0.37 to 0.45 for 316L powder layer, and in a range from 0.37 to 0.4 for IN718 powder layer.
Among additive manufacturing (AM) technologies, the laser powder bed fusion (L-PBF) is one of the most important technologies to produce metallic components. The layer-wise build-up of components and the complex process conditions increase the probability of the occurrence of defects. However, due to the iterative nature of its manufacturing process and in contrast to conventional manufacturing technologies such as casting, L-PBF offers unique opportunities for in-situ monitoring. In this study, two cameras were successfully tested simultaneously as a machine manufacturer independent process monitoring setup: a high-frequency infrared camera and a camera for long time exposure, working in the visible and infrared spectrum and equipped with a near infrared filter. An AISI 316L stainless steel specimen with integrated artificial defects has been monitored during the build. The acquired camera data was compared to data obtained by computed tomography. A promising and easy to use examination method for data analysis was developed and correlations between measured signals and defects were identified. Moreover, sources of possible data misinterpretation were specified. Lastly, attempts for automatic data analysis by data Integration are presented.
The capability to produce complexly and individually shaped metallic parts is one of the main advantages of the laser powder bed fusion (PBF LB/M) process. Development of material and machine specific process parameters is commonly based on results acquired from small cubic test coupons of about 10 mm edge length. Such cubes are usually used to conduct an optimization of process parameters to produce dense material. The parameters are then taken as the basis for the manufacturing of real part geometries. However, complex geometries go along with complex thermal histories during the manufacturing process, which can significantly differ from thermal conditions prevalent during the production of simply shaped test coupons. This may lead to unexpected and unpredicted local inhomogeneities of the microstructure and defect distribution in the final part and it is a root cause of reservations against the use of additive manufacturing for the production of safety relevant parts. In this study, the influence of changing thermal conditions on the resulting melt pool depth of 316L stainless steel specimens is demonstrated. A variation of thermo-graphically measured intrinsic preheating temperatures was triggered by an alteration of inter layer times and a variation of cross section areas of specimens for three distinct sets of process parameters. Correlations between the preheating temperature, the melt pool depth, and occurring defects were analyzed. The limited expressiveness of the results of small density cubes is revealed throughout the systematic investigation. Finally, a clear recommendation to consider thermal conditions in future process parameter optimizations is given.
Laser powder bed fusion (L-PBF) is the most prominent additive manufacturing (AM) technology for metal part production. Among the high number of factors influencing part quality and mechanical properties, the inter layer time (ILT) between iterative melting of volume elements in subsequent layers is almost completely unappreciated in the relevant literature on L-PBF. This study investigates the effect of ILT with respect to build height and under distinct levels of volumetric energy density (VED) using the example of 316L stainless steel. In-situ thermography is used to gather information on cooling conditions during the process, which is followed by an extensive metallographic analysis. Significant effects of ILT and build height on heat accumulation, sub-grain sizes, melt pool geometries and hardness are presented. Furthermore, the rise of defect densities can be attributed to a mutual interplay of build height and ILT. Hence, ILT has been identified as a crucial factor for L-PBF of real part components especially for those with small cross sections.
The presented work discusses the accuracy of Laser Induced Breakdown Spectroscopy (LIBS) in determining the total chloride content in cement pastes. LIBS as an emission spectroscopy method is used to detect simultaneously several elements present in cement-based materials. By scanning surfaces the variability in the spatial distribution of elements can be visualised. However, for a quantification of the results, studies are necessary to characterise possible influences due to the wide variation of the chemical compositions in which cement can occur. It is shown how the calibration can be done, how the calibration samples were produced, and which statistical parameters are necessary to describe the precision of the regression. The performance of LIBS is estimated by detecting chloride in validation samples. Therefore, 55 samples and 7 ets with changing mix ompositions were produced. The presented study deals with possible influences of different mix compositions, ncluding different cations of chloride, varying w/c-ratios and the artial replacement of Portland cement with last furnace slag (50% BFS) and limestone (30% LS). Comparing the LIBS results with otentiometric titration, n accuracy of±0.05 wt%/total has been determined.
The determination of chloride is still one of the main tasks for the evaluation of reinforced concrete structures.
The corrosion of the reinforcement induced by the penetrating chlorides is the dominant damage process affecting the lifetime of concrete structures. In the recent years different research groups demonstrated that LIBS can be a fast and reliable method to quantify chlorine in cement-bound materials. Because chlorine in concrete can only occur as solved ions in the pore solution or bound in salts or hydrated cement phases, the detected emission of chlorine can be correlated with the chloride concentration determined e.g. with potentiometric titration. This work inter alia describes the production of reference samples and possible side effects during the production process. Due to transport processes in the porous matrix of the cement a misinterpretation of the concentrations is possible. It is shown how to overcome these effects and higher precisions of the single measurements can be realised. Using the calibration method, blank sample method and noise method, three different ways of calculating the limit of detection (LOD) and limit of quantification (LOQ) are compared. Due to the preparation of the reference samples a precision of the whole calibration model of sx0 = 0.023 wt% is determined.
The validation of the model is based on different test sets, which are varying in their composition
(different Cl-salts, water-to-cement ratios and additives). The determined mean error of the validation is
0.595 ± 0.063 wt%, which is comparable to standardised methods like potentiometric titration, direct potentiometry
or photometry (0.40 ± 0.06 wt%) [1].
Additively manufactured test specimens made of polyamide 12 (PA 12) by Laser Sintering as well as of acrylonitrile butadiene styrene (ABS) by Fused Layer Modelling, were characterised with active thermography directly after manufacturing and after artificial weathering. For this, two different excitation methods (flash and pulse heating) were used and compared, regarding their suitability for the detection of constructed and imprinted defects inside the test specimens. To increase the quality of the thermograms, data processing methods like thermal signal reconstruction (TSR) and Fourier Transformation after TSR were applied. To further investigate the long-term stability of the additively manufactured test specimens towards environmental stress, like UV radiation, heat, humidity, water contact and frost with active thermography, an artificial weathering test over 2000 hours (~3 months) was applied to the specimens. The monitoring of the changes in the optical properties of the weathered plastics was supplemented by spectral reflectance and UV/VIS spectroscopy.
For assuring the safety and reliability of components and constructions in energy applications made of fiber-reinforced polymers (e.g., blades of wind turbines and tidal power plants, engine chassis, flexible oil and gas pipelines) innovative non-destructive testing methods are required. Within the European Project VITCEA complementary methods (shearography, microwave, ultrasonics and thermography) have been further developed and validated. Together with partners from the industry, test specimens have been constructed and selected on-site containing different artificial and natural defect artefacts. As base materials, carbon and glass fibers in different orientations and layering embedded in different matrix materials (epoxy, polyamide) have been considered. In this contribution, the validation of flash and lock-in thermography to these testing problems is presented. Data analysis is based on thermal contrasts and phase evaluation techniques. Experimental data are compared to analytical and numerical models. Among others, the influence of two different types of artificial defects (flat bottom holes and delaminations) with varying diameters and depths and of two different materials (CFRP and GFRP) with unidirectional and quasi-isotropic fiber alignment is discussed.
Low-velocity impact damages were monitored in-situ using an infrared camera before, during and after Impact load. Thermal images were recorded as a function of time at the front side (impact) as well as at the rear side of the plates using a high frame rate. In CFRP and GFRP specimens with different thicknesses and made of various types of fibers and matrix materials, different kind of damages were observed. The sizes of the heated areas being related to the damages and the amount of energy dissipated into heat was determined quantitatively as a function of impact energy and are a measure of the resistance of the different materials against impact load.
In this work, we present a novel approach to photothermal super resolution based thermographic resolution of internal defects using two-dimensional pixel pattern-based active photothermal laser heating in conjunction with subsequent numerical reconstruction to achieve a high-resolution reconstruction of internal defect structures. With the proposed adoption of pixelated patterns generated using laser coupled high-power DLP projector technology the complexity for achieving true two-dimensional super resolution can be dramatically reduced taking a crucial step forward towards widespread practical viability. Furthermore, based on the latest developments in high-power DLP projectors, we present their first application for structured pulsed thermographic inspection of macroscopic metal samples. In addition, a forward solution to the underlying inverse problem is proposed along with an appropriate heuristic to find the regularization parameters necessary for the numerical inversion in a laboratory setting. This allows the generation of synthetic measurement data, opening the door for the application of machine learning based methods for future improvements towards full automation of the method. Finally, the proposed method is experimentally validated and shown to outperform several established conventional thermographic testing techniques while conservatively improving the required measurement times by a factor of 8 compared to currently available photothermal super resolution techniques.
In this work, the limits of using spatially structured heating combined with subsequent photothermal super resolution reconstruction for the thermographic detection and resolution of internal defects are investigated. The approach is based on the application of modern high-power laser projector technology, which is used to repeatedly project varying spatially structured 2D pixel patterns to photothermally heat the object under test. After processing the generated thermographic data using nonlinear convex optimisation in conjunction with exploiting the joint-sparse nature of the defect signals within the individual measurements, a high-resolution 2D-sparse defect/inhomogeneity map is obtained. The main focus of the investigation is set on the influence of the number of individual measurements on the achievable reconstruction quality. Using numerical simulations based on an analytical representation of the forward solution to the underlying inverse problem, the convergence rate over performed measurements of the achievable reconstruction quality is determined. Finally, all findings are experimentally validated by reconstructing a set of internal defects in an additively manufactured sample. In this work, it is shown that for a variety of different defect separation distances, the projection of 50 different pixel patterns allows for a good trade-off between experimental complexity and reconstruction quality.
Thermographic photothermal super resolution reconstruction enables the resolution of internal defects/inhomogeneities below the classical limit, which is governed by the diffusion properties of thermal wave propagation. Based on a combination of the application of special sampling strategies and a subsequent numerical optimization step in post-processing, thermographic super resolution has already proven to be superior to standard thermographic methods in the detection of one-dimensional defect/inhomogeneity structures. In our work, we report an extension of the capabilities of the method for efficient detection and resolution of defect cross sections with fully two-dimensional structured laser-based heating. The reconstruction is carried out using one of two different algorithms that are proposed within this work. Both algorithms utilize the combination of several coherent measurements using convex optimization and exploit the sparse nature of defects/inhomogeneities as is typical for most nondestructive testing scenarios. Finally, the performance of each algorithm is rated on reconstruction quality and algorithmic complexity. The presented experimental approach is based on repeated spatially structured heating by a high power laser. As a result, a two-dimensional sparse defect/inhomogeneity map can be obtained. In addition, the obtained results are compared with those of conventional thermographic inspection methods that make use of homogeneous illumination. Due to the sparse nature of the reconstructed defect/inhomogeneity map, this comparison is performed qualitatively.
This paper studies the dispersion of GPR wave’s phase velocity at different wideband frequencies in plywood and concrete with varying moisture content. This study makes use of two GPR antennas with 2 GHz centre frequency operating in wide angle reflection and refraction (WARR) mode and with computation of spectral analysis of the surface wave (SASW). Computation of phase velocities is based on the Acquisition of the cross-power spectrum and phase unwrap of two distorted ground waves at positions Closer to and farther away from the transmitting antenna. The velocities of the ground waves are found to experience greater dispersion in low frequency regimes within the effective frequency bandwidths determined and thresholded by time–frequency analysis (TFA) and coherence plotting of the ground waves.
This study validates not only the methodology, but also identifies the optimal distance between the first (Rx1) and second (Rx2) receivers as k/2, which is based on a fixed transmitter (Tx) minus the first Receiver (Rx1) distance. It serves as an indication of changeable separation distance when other lower frequency GPR is used because the distances of Tx-Rx1 and Rx1-Rx2 are wavelength dependent and thus also frequency dependent. Effects of moisture contents and chloride contamination in concrete were also characterized according to the dispersion plots . that wave traelling in lower frequencies in GPR wave is much decelerated than that in higher frequencies. This research also contributes to the building of the ‘‘GPRWARR machine” suggested in Annan and Jackson (2017) [1], within which the effects of wave Dispersion on phase velocity can be inversely modelled to characterize variations in the material properties of infrastructure as a means of detecting surface damage.
Classification of Spot-Welded Joints in Laser Thermography Data Using Convolutional Neural Networks
(2021)
Spot welding is a crucial process step in various industries. However, classification of spot welding quality is still a tedious process due to the complexity and sensitivity of the test material, which drain conventional approaches to its limits. In this article, we propose an approach for quality inspection of spot weldings using images from laser thermography data. We propose data preparation approaches based on the underlying physics of spot-welded joints, heated with pulsed laser thermography by analyzing the intensity over time and derive dedicated data filters to generate training datasets. Subsequently, we utilize convolutional neural networks to classify weld quality and compare the performance of different models against each other. We achieve competitive results in terms of classifying the different welding quality classes compared to traditional approaches, reaching an accuracy of more than 95 percent. Finally, we explore the effect of different augmentation methods.
We present an in situ triple coupling of synchrotron X-ray diffraction with Raman spectroscopy, and thermography to study milling reactions in real time. This combination of methods allows a correlation of the structural evolution with temperature information. The temperature information is crucial for understanding both the thermodynamics and reaction kinetics. The reaction mechanisms of three prototypical mechanochemical syntheses, a cocrystal formation, a C@C bond formation (Knoevenagel condensation), and the formation
of a manganese-phosphonate, were elucidated. Trends in the temperature development during milling are identified. The heat of reaction and latent heat of crystallization of the product contribute to the overall temperature increase. A decrease in temperature occurs via release of, for example, water as a byproduct.
Solid and liquid intermediates are detected. The influence of the mechanical impact could be separated from temperature effects caused by the reaction.
Dehydration is a common process occurring in natural environments, rocks, and building materials. Suitable methods for monitoring the changing moisture content should be identified.
We have investigated the impact of dehydration of sandstones on complex conductivity spectra. Spectral induced polarization measurements were performed on five sandstone samples during evaporative drying. The complex conductivity spectra indicate considerable changes with decreasing saturation. The relationship between conductivity and saturation can be described by a power law known as the second empirical Archie equation. Separate saturation exponents have been determined for the real and imaginary parts of conductivity. The imaginary part of conductivity indicates higher saturation exponents for the investigated sandstones compared to the real part. Obviously, the saturation exponents depend on the method used for changing water saturation. Evaporative drying, which is used in our experiments, causes an increase of pore-water salinity and results in lower saturation exponents for the real part of conductivity but higher exponents for the imaginary part in comparison with the classical imbibition/Drainage technique. We evaluate a theoretical approach that considers the influence of pore water salinity on the saturation exponents of the real and imaginary parts of conductivity. The complex conductivity spectra are processed by a Debye decomposition procedure.
The resulting integrating parameters such as direct current resistivity, total chargeability, normalized chargeability, and mean relaxation time indicate a power law dependence on saturation. Our experiments indicate that the imaginary part of conductivity and normalized chargeability have a high sensitivity related to the drying process of rocks and might be suitable indicators to Monitor the changes of moisture content in sandstones.
Integration of fiber reinforcement in high-performance cementitious materials has become widely applied in many fields of construction. One of the most investigated advantages of steel Fiber reinforced concrete (SFRC) is the deceleration of crack growth and hence its improved sustainability.
Additional benefits are associated with its structural properties, as fibers can significantly increase the ductility and the tensile strength of concrete. In some applications it is even possible to entirely replace the conventional reinforcement, leading to significant logistical and environmental benefits.
Fiber reinforcement can, however, have critical disadvantages and even hinder the Performance of concrete, since it can induce an anisotropic material behavior of the mixture if the fibers are not appropriately oriented. For a safe use of SFRC in the future, reliable non-destructive testing (NDT) methods need to be identified to assess the fibers’ orientation in hardened concrete. In this study,
ultrasonic material testing, electrical impedance testing, and X-ray computed tomography have been investigated for this purpose using specially produced samples with biased or random Fiber orientations. We demonstrate the capabilities of each of these NDT techniques for fiber orientation measurements and draw conclusions based on these results about the most promising areas for future research and development.
NMR is becoming increasingly popular for the investigation of building materials as it is a non-invasive technology that does not require any sample preparation nor causes damage to the material. Depending on the specific application it can offer insights into properties like porosity and spatial saturation degree as well as pore structure. Moreover it enables the determination of moisture transport properties and the (re-)distribution of internal moisture into different reservoirs or chemical phases upon damage and curing. However, as yet most investigations were carried out using devices originally either designed for geophysical applications or the analysis of rather homogeneous small scale (< 10 mL) samples. This paper describes the capabilities of an NMR tomograph, which has been specifically optimized for the investigation of larger, heterogeneous building material samples (diameters of up to 72 mm, length of up to 700 mm) with a high flexibility due to interchangeable coils allowing for a high SNR and short echo times (50 - 80 m s).
Relaxation phenomena observed in the electrical low-frequency range (approximately 1 mHz-10 kHz) of natural porous media like sandstones is often assumed to be directly related to the dominant (modal) pore throat sizes measured, for instance, with mercury intrusion porosimetry. Attempts to establish a universally valid relationship between pore size and peak Spectral Induced Polarization (SIP) relaxation time have failed, considering sandstones from very different origins and featuring great variations in textural and chemical compositions as well as in geometrical pore space properties. In addition working with characteristic relaxation times determined in Cole-Cole or Debye decomposition fits to build the relationship have not been successful. In particular, samples with narrow pore throats are often characterized by long SIP relaxation times corresponding to long “characteristic length scales” in these media, assuming that the diffusion coefficients along the electrical double layer were constant. Based on these observations, three different types of SIP relaxation can be distinguished. We present a new way of assessing complex pore spaces of very different sandstones in a multi-methodical approach to combine the benefits of mercury intrusion porosimetry, micro-computed tomography, and nuclear magnetic resonance. In this way, we achieve much deeper insight into the pore space due to the different resolutions and sensitivities of the applied methods to both pore constrictions (throats) and wide pores (pore bodies). We experimentally quantify pore aspect ratios and volume distributions within the two pore regions. We clearly observe systematic differences between three SIP relaxation types identified previously and can attribute the SIP peak relaxation times to measured characteristic length scales within our materials. We highlight selected results for a total of nine sandstones. It seems that SIP relaxation behavior depends on the size difference of the narrow pore throats to the wide pore bodies, which increases from SIP Type 1 to Type 3.
A concept for the application of a magnetocaloric device in energy-efficient air conditioners is introduced. In order to evaluate this concept, a test stand has been developed equipped with a magnetic field source providing about a 1.5-T flux density change into which different Regenerator geometries can be implemented and evaluated. A processing route for the production of profiled magnetocaloric LaFeSiMn-based composite plates by tape casting is presented. The processed plates show a maximum isothermal entropy change of about 3.8 J kg−1 K−1 at a magnetic field change of 1.5 T at 285 K. The hydraulic and thermal performance of regenerator geometries that can be realized by profiled plates is assessed by calculations.
Magnetocaloric composite wires have been studied by pulsed-field measurements up to μ0ΔH = 10 T with a typical rise time of 13 ms in order to evaluate the evolution of the adiabatic temperature change of the core, ΔTad, and to determine the effective temperature change at the surrounding steel jacket, ΔTeff, during the field pulse. An inverse thermal hysteresis is observed for ΔTad due to the delayed thermal transfer. By numerical simulations of application-relevant sinusoidal magnetic field profiles, it can be stated that for field-frequencies of up to two field cycles per second heat can be efficiently transferred from the core to the outside of the jacket. In addition, intense numerical simulations of the temperature change of the core and jacket were performed by varying different parameters, such as frequency, heat capacity, thermal conductivity and interface resistance in order to shed light on their impact on ΔTeff at the outside of the jacket in comparison to ΔTad provided by the core.
The knowledge of the thermal parameters of a particular concrete is essential for thermal design of a building, but also could help to identify and assess the state of a concrete structure. Active thermography has the potential to be applied onsite and to provide a fast investigation of thermal properties. In this work, three different concrete samples were investigated by active thermography in reflection and in transmission setup. It was found that this method yields the same results without direct contact as the Transient Plane Source (TPS) method as an established inspection tool.
Bei der Realisierung von Oberflächen-Schutz-System (OSS) auf Betonflächen ist die tatsächlich erreichte Dicke der Beschichtung ein wesentlicher Parameter, der über die Funktionalität und Langzeitstabilität der aufgetragenen Schicht entscheidet. Die Firma IBOS und die BAM haben in den letzten Jahren gemeinsam einen funktionstüchtigen Prototyp eines Messgeräts zur zerstörungsfreien Schichtdickenmessung für Bodenbeschichtungen entwickelt. Hierin wird die Abkühlkurve eines vorher erwärmten Bereichs berührungslos mit einer IR-Kamera erfasst und mit einem Modell verglichen, was die Bestimmung der Schichtdicke ermöglicht. Im Rahmen dieses Beitrags wird die Umsetzung der Methode unter Laborbedingungen und in der Praxis auf der Baustelle erläutert und diskutiert.
The knowledge about energy release and transient behaviour of flash lamps, used as short-term energy source in flash thermography investigations, is essential for a quantitative analysis of the results as well as for simulations regarding such experiments. Usually, only a rectangular pulse shape of a typical duration of a few milliseconds is regarded when flash lamps pulses are described. This note considers the afterglow period of a flash lamp explicitly and shows that negligence of this afterglow effect leads to a systematic underestimation of the pulse length and the related total energy release. In the reported case, the intensity transient recorded by a Silicon photo diode was transformed into a temperature transient, allowing the study of the real energy release. It turns out that approximately only 50% of the entire pulse energy were released during the usually regarded period of 2.8 ms, while the other half was released within a period of 20 ms with an exponential decay.
Recently, the photothermal determination of the thermophysical properties of concrete under lab conditions was reported. Their values are mainly needed to look at the energy consumption of buildings. Additionally, changes in their values in relation to the initial state might also be a good indicator for material quality or for moisture. The present contribution explains the photothermal method in a more general way to indicate the potential for on-site application. Secondly, a special application case is regarded: the detection of moisture in concrete. Two concrete samples were soaked with water, followed by a drying period, to obtain different levels of water penetration. The water contents were determined by weighing, and the photothermal response was measured. The results show a large influence on the measured temperature transients, which is larger than expected from the original simple model. They clearly provide two points: the photothermal method is suited to detect moisture in concrete, but the magnitude of the actual measurement effect is not yet understood.
High quality tubular products are essential to the oil and gas industry. Quality control during their production focuses on the non-destructive detection of surface defects. The structured light technique is a candidate for the challenge to detect, monitor and evaluate such defects in real-time.
In the present study the automatic processing of structured light measurements is performed and validated. The algorithm for the automatic Analysis of inspection data has an advantage over current data evaluation methods based on individual assessments of operators.
LIBS‐ConSort: Development of a sensor‐based sorting method for construction and demolition waste
(2023)
AbstractA joint project of partners from industry and research institutions approaches the challenge of construction and demolition waste (CDW) sorting by investigating and testing the combination of laser‐induced breakdown spectroscopy (LIBS) with near‐infrared (NIR) spectroscopy and visual imaging. Joint processing of information (data fusion) is expected to significantly improve the sorting quality of various materials like concrete, main masonry building materials, organic components, etc., and may enable the detection and separation of impurities such as SO3‐cotaining building materials (gypsum, aerated concrete, etc.)Focusing on Berlin as an example, the entire value chain will be analyzed to minimize economic / technological barriers and obstacles at the cluster level and to sustainably increase recovery and recycling rates.The objective of this paper is to present current progress and results of the test stand development combining LIBS with NIR spectroscopy and visual imaging. In the future, this laboratory prototype will serve as a fully automated measurement setup to allow real‐time classification of CDW on a conveyor belt.
To date, the destructive extraction and analysis of drilling cores is the main possibility to obtain depth information about damaging water ingress in building floors. The time- and costintensive procedure constitutes an additional burden for building insurances that already list piped water damage as their largest item. With its high sensitivity for water, a ground-penetrating radar (GPR) could provide important support to approach this problem in a non-destructive way. In this research, we study the influence of moisture damage on GPR signals at different floor constructions. For this purpose, a modular specimen with interchangeable layers is developed to vary the screed and insulation material, as well as the respective layer thickness. The obtained data set is then used to investigate suitable signal features to classify three scenarios: dry, damaged insulation, and damaged screed. It was found that analyzing statistical distributions of A-scan features inside one B-scan allows for accurate classification on unknown floor constructions. Combining the features with multivariate data analysis and machine learning was the key to achieve satisfying results. The developed method provides a basis for upcoming validations on real damage cases.
When applying Ground Penetrating Radar (GPR) to assess the moisture content of building materials, different medium properties, dimensions, interfaces and other unknown influences may require specific strategies to achieve useful results. Hence, we present an overview of the various approaches to carry out moisture measurements with GPR in civil engineering (CE). We especially focus on the applied Signal features such as time, amplitude and frequency features and discuss their limitations. Since the majority of publications rely on one single feature when applying moisture measurements, we also hope to encourage the consideration of approaches that combine different signal features for further developments.
Industry 4.0 is all about interconnectivity, sensor-enhanced process control, and data-driven systems. Process analytical technology (PAT) such as online nuclear magnetic resonance (NMR) spectroscopy is gaining in importance, as it increasingly contributes to automation and digitalization in production. In many cases up to now, however, a classical evaluation of process data and their transformation into knowledge is not possible or not economical due to the insufficiently large datasets available. When developing an automated method applicable in process control, sometimes only the basic data of a limited number of batch tests from typical product and process development campaigns are available. However, these datasets are not large enough for training machine-supported procedures. In this work, to overcome this limitation, a new procedure was developed, which allows physically motivated multiplication of the available reference data in order to obtain a sufficiently large dataset for training machine learning algorithms. The underlying example chemical synthesis was measured and analyzed with both application-relevant low-field NMR and high-field NMR spectroscopy as reference method. Artificial neural networks (ANNs) have the potential to infer valuable process information already from relatively limited input data. However, in order to predict the concentration at complex conditions (many reactants and wide concentration ranges), larger ANNs and, therefore, a larger Training dataset are required. We demonstrate that a moderately complex problem with four reactants can be addressed using ANNs in combination with the presented PAT method (low-field NMR) and with the proposed approach to generate meaningful training data.
The quality of additively manufactured (AM) parts is determined by the applied process parameters used and the properties of the feedstock powder. The influence of inner gas pores in feedstock particles on the final AM product is a phenomenon which is difficult to investigate since very few non-destructive measurement techniques are accurate enough to resolve the micropores.
3D X-ray computed tomography (XCT) is increasingly applied during the process chain of AM parts as a non-destructive monitoring and quality control tool and it is able to detect most of the pores.
However, XCT is time-consuming and limited to small amounts of feedstock powder, typically a few milligrams. The aim of the presented approach is to investigate digital radiography of AM feedstock particles as a simple and fast quality check with high throughput. 2D digital radiographs were simulated in order to predict the visibility of pores inside metallic particles for different pore and particle diameters. An experimental validation was performed. It was demonstrated numerically and experimentally that typical gas pores above a certain size (here: 3 to 4.4 µm for the selected X-ray setup), which could be found in metallic microparticles, were reliably detected by digital radiography.
Large-language models (LLMs) such as GPT-4 caught the interest of many scientists. Recent studies suggested that these models could be useful in chemistry and materials science. To explore these possibilities, we organized a hackathon. This article chronicles the projects built as part of this hackathon. Participants employed LLMs for various applications, including predicting properties of molecules and materials, designing novel interfaces for tools, extracting knowledge from unstructured data, and developing new educational applications. The diverse topics and the fact that working prototypes could be generated in less than two days highlight that LLMs will profoundly impact the future of our fields. The rich collection of ideas and projects also indicates that the applications of LLMs are not limited to materials science and chemistry but offer potential benefits to a wide range of scientific disciplines.
The photoacoustic measurement technique is a powerful yet underrepresented method to characterize the thermal transport properties of thin films. For the case of isotropic low thermal diffusivity samples, such as glasses or polymers, we demonstrate a general approach to extract the thermal conductivity with a high degree of significance. We discuss in particular the influence of thermal effusivity, thermal diffusivity, and sample layer thickness on the significance and accuracy of this measurement technique. These fundamental thermal properties guide sample and substrate selection to allow for a feasible thermal transport characterization. Furthermore, our data evaluation allows us to directly extract the thermal conductivity from this transient technique, without separate determination of the volumetric heat capacity, when appropriate boundary conditions are fulfilled.
Using silica, poly(methyl methacrylate) (PMMA) thin films, and various substrates (quartz, steel, and silicon), we verify the quantitative correctness of our analytical approach.
Learned block iterative shrinkage thresholding algorithm for photothermal super resolution imaging
(2022)
Block-sparse regularization is already well known in active thermal imaging and is used for multiple-measurement-based inverse problems. The main bottleneck of this method is the choice of regularization parameters which differs for each experiment. We show the benefits of using a learned block iterative shrinkage thresholding algorithm (LBISTA) that is able to learn the choice of regularization parameters, without the need to manually select them. In addition, LBISTA enables the determination of a suitable weight matrix to solve the underlying inverse problem. Therefore, in this paper we present LBISTA and compare it with state-of-the-art block iterative shrinkage thresholding using synthetically generated and experimental test data from active thermography for defect reconstruction. Our results show that the use of the learned block-sparse optimization approach provides smaller normalized mean square errors for a small fixed number of iterations. Thus, this allows us to improve the convergence speed and only needs a few iterations to generate accurate defect reconstruction in photothermal super-resolution imaging.
Machine learning based multi-sensor fusion for the nondestructive testing of corrosion in concrete
(2020)
Half-cell potential mapping (HP) is the most popular non-destructive testing method (NDT) for locating corrosion damage in concrete. It is generally accepted that HP is susceptible to environmental factors caused by salt-related deterioration, such as different moisture and chloride gradients. Additional NDT methods are able to identify distinctive areas but are not yet used to estimate more accurate test results. We present a Supervised Machine Learning (SML) based approach to data fusion of seven different signal features to obtain higher quality information. SMLs are methods that explore (or learn) relationships between different (sensor) data from predefined data labels. To obtain a representative, labelled data set we conducted a comprehensive experiment simulating the deterioration cycle of a chloride exposed device in the laboratory. Our data set consists of 18 measurement campaigns, each containing HP, Ground Penetrating- Radar, Microwave Moisture and Wenner resistivity data. We compare the performance of different ML approaches. Many outperform the best single method, HP. We describe the intrinsic challenges posed by a data-driven approach in NDT and show how future work can help overcome them.
In this work the global and local mechanical properties of the magnetocaloric intermetallic LaFe11.2Si1.8 alloy are investigated by a combination of different testing and characterization techniques in order to shed light on the partly contradictory data in recent literature. Macroscale compression tests were performed to illuminate the global fracture behavior and evaluate it statistically. LaFe11.2Si1.8 demonstrates a brittle behavior with fracture strains below 0.6% and widely distributed fracture stresses of 180–620 MPa leading to a Weibull modulus of m = 2 to 6. The local mechanical properties, such as hardness and Young's modulus, of the main and secondary phases are examined by nanoindentation and Vickers microhardness tests. An intrinsic strength of the main magnetocaloric phase of at least 2 GPa is estimated. The significantly lower values obtained by compression tests are attributed to the detrimental effect of pores, microcracks, and secondary phases. Microscopic examination of indentation-induced cracks reveals that ductile α-Fe precipitates act as crack arrestors whereas pre-existing cracks at La-rich precipitates provide numerous ‘weak links’ for the initiation of catastrophic fracture. The presented systematic study extends the understanding of the mechanical reliability of La(Fe, Si)13 alloys by revealing the correlations between the mechanical behavior of macroscopic multi-phase samples and the local mechanical properties of the single phases